code: def causal_conv(value, filter_, dilation, name="causal_conv"):
      conv_op = lambda x: tf.nn.conv1d(
          input=x, filters=filter_, stride=1, padding="VALID"
      )
      with tf.compat.v1.name_scope(name):
          if dilation > 1:
              transformed, pad_elements = time_to_batch(value, dilation)
              conv = conv_op(transformed)
              restored = batch_to_time(conv, dilation)
              restored = restored[
                  :, : tf.shape(input=restored)[1] - pad_elements, :
              ]
          else:
              restored = conv_op(value)
          return restored
  
file_path: finetune/finetune/base_models/oscar/featurizer.py
imports: import tensorflow
  from finetune.base_models.gpt.featurizer import dropout, embed, split_heads, merge_heads
  from finetune.util.shapes import shape_list, lengths_from_eos_idx
  from finetune.custom_ops import recursive_agg
  from finetune.base_models.gpt.featurizer import get_pos_values
  from finetune.optimizers.recompute_grads import recompute_grad
  import functools
label_desc: Use image node_id 6 for calling a global function with example usage: causal_conv(value, filter_, dilation, name) and returns: restored